DeepCOVID: An Operational Deep Learning-Driven Framework for Explainable Real-Time COVID-19 Forecasting

Alexander Rodríguez, Anika Tabassum, Jiaming Cui, Jiajia Xie, Javen Ho, Pulak Agarwal, Bijaya Adhikari, B. Aditya Prakash

AAAI 2021 pp. 15393-15400

doi:10.1609/AAAI.V35I17.17808 /aaai/2021/rodriguez2021aaai-deepcovid/

AAAI Semantic Scholar

Cite

Text

Rodríguez et al. "DeepCOVID: An Operational Deep Learning-Driven Framework for Explainable Real-Time COVID-19 Forecasting." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I17.17808

Markdown

[Rodríguez et al. "DeepCOVID: An Operational Deep Learning-Driven Framework for Explainable Real-Time COVID-19 Forecasting." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/rodriguez2021aaai-deepcovid/) doi:10.1609/AAAI.V35I17.17808

BibTeX

@inproceedings{rodriguez2021aaai-deepcovid,
  title     = {{DeepCOVID: An Operational Deep Learning-Driven Framework for Explainable Real-Time COVID-19 Forecasting}},
  author    = {Rodríguez, Alexander and Tabassum, Anika and Cui, Jiaming and Xie, Jiajia and Ho, Javen and Agarwal, Pulak and Adhikari, Bijaya and Prakash, B. Aditya},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {15393-15400},
  doi       = {10.1609/AAAI.V35I17.17808},
  url       = {https://mlanthology.org/aaai/2021/rodriguez2021aaai-deepcovid/}
}